Map # introduction write an intro to convey the issue the goal of the article :be careful of bikes

When Are Bikes at the Greatest Risk? A Look at Seasonal and weekly Patterns

What Makes a Bike a Target? Analyzing Theft by Bicycle Type and Value

Discuss any available data on bike types, brands, or values, showing if certain bikes are more vulnerable to theft than others.

A Decade of Disappearing Bikes: Tracing Theft Trends Over Ten Years

Displays the count of various bicycle-related offences reported to the Toronto Police Service, highlighting categories such as stolen, found, and other incidents. The smooth increase in bar heights emphasizes the scale of each offence type, offering insights into the distribution and relative frequency of these incidents from 2014 to 2024

Visualizes the monthly count of bicycle thefts reported to the Toronto Police Service from 2014 to June 2024. The animation reveals seasonal patterns and variations across different years, providing insights into peak months and potential trends over time.

Mapping the Danger Zones: High-Risk Locations Across Toronto

Use a map visualization to highlight theft hotspots across Toronto, pointing out neighborhoods or transit hubs with the highest incidence rates.

Discussion

Consolidation of all the info that we present, we first talk about…this is how all visualizations are connected together, in the future what can be done…

Conclusion

short reminder for the audience, tips to protect their bikes

General tips: - This paper wants to tell the story about tips to protect bikes

after every visualizations talk a bit about the visualization itself, key points, make sure the story that we are talking about is proved by this visualization so each plot make sense (for example in 2016 there is a peak in May illustrated by the visualization, why? find relevant news and articels to explain it and cite), 用更descriptive的形式去explain 你的visualization plot with proves.

Ride at Your Own Risk: Insights into Toronto’s Bicycle Theft Hotspots

[To Be Updated….]